Multilabel prediction of virus target proteins via multimodal graph representation learning

doi:10.1371/journal.pcbi.1014320

Multilabel prediction of virus target proteins via multimodal graph representation learning

Fig 2

Feature analysis and comparison on the dataset.

Statistical analysis of traditional features: (A) amino acid composition, (B) ratio, (C) predicted coil proportion, and (D) closeness. (E) AUPRs achieved by different types of traditional features. (F) SHAP analysis of traditional features. (G) t-SNE visualization of global topological properties for VTPs and non-VTPs. (H) Gene ontology similarity among VTPs and between VTPs and non-VTPs for each virus. Significant differences are evaluated using Wilcoxon rank sum tests. **** p < 0.0001, *** 0.0001 ≤ p < 0.001, ** 0.001 ≤ p < 0.01, * 0.01 ≤ p < 0.05, and ns: p ≥ 0.05. (I) Performance of different protein embeddings.

doi: https://doi.org/10.1371/journal.pcbi.1014320.g002